Using a combined machine learning and bio-automation approach to understand, recognise and control the bacterial stress landscape

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This research aims to better understand the regulatory changes that bacteria undergo whilst experiencing stress and how to harness these changes for producing bacterial stress reporter strains. Specifically, the research is focused the concept of load-stress: the metabolic exertion of producing large amounts of a given product e.g. a highly expressed protein. The chosen organism for this project is Escherichia coli which is widely used in industry and is considered to be the model organism for gram-negative bacteria, as such is very well characterised and is frequently used in research. Investigations into the stress landscape of bacteria is still relatively novel, therefore, this project is expected to have an impact that will assist with further research especially in synthetic biology and the biotechnology industries. Additionally, this knowledge can be applied in an industrial setting where yield and efficiency can be improved given a better understanding of the impact of load-stress.The research will aim to recognise, respond to, and define the regulatory fingerprints of stress responses including load stress. To achieve this aim, a system will be required that can induce load-stress in bacteria as well as a system to monitor and measure the regulatory and physiological impacts stress at a global and single cell level. Inducing load stress in bacteria will be accomplished by introducing paired synthetic constructs (i) that promote expression of a range of heterologous proteins (ii) monitor the effects of expression of differing heterologous on the regulatory architecture of the cell using synthetic and natural promoters. Machine learning and optimisation algorithms will aid the design of experiments while bio-automation will allow a greater scale of measurement and ultimately hypothesis testing. The analysis of results will require a significant amount of informatics in order to explore the regulatory networks at different levels of response from two components systems to regulons and promoter architectures. It is intended to develop new methodology in these areas that can be generalised for wider application outside of this study. Ultimately, the outcome of this research could help the biotechnology industry increase the efficiency of the production of useful compounds and will help us understand more about the way bacteria respond to stress.